Leveraging Data for Demand Generation Success

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Leveraging Data for Demand Generation Success

  1. 1. Leveraging Data forDemand Generation Success
  2. 2. Background: Since 1990, Winn Technology Since 2001, WaveLength Market Group has supported over 800 Analytics: Specializing in combiningtechnology firms with thousands knowledge of technology markets,of demand generation solutions. products and services with data management & quantitative analysis for strategies & programs that deliver superior results.
  3. 3. Speakers: Geoffrey Swallow President Winn Technology Group Kate Healy32 years experience in technology Principal marketing Wavelength Analytics 19 years experience in technology marketing
  4. 4. •Challenges: Need to optimize marketing and sales budgets • Data feast or data famine • Prospect quantity vs. prospect quality
  5. 5. Agenda:• Data Analytic Strategies • Pre-Campaign • Real Time • Post-Campaign • Data Strategies to Drive Quotas • Using Intelligence from Data • More about the “Ideal Prospect” Algorithm • Strategies for Sourcing and Managing Data
  6. 6. Pre-Campaign Analytics Target Universe Data Strategies • Validity • Aligning expectation • Refining • Modeling
  7. 7. Pre-Campaign Case 1 Fortune 500 Software Firm User Conference Event Promotion Impact of Data Validity• Program Objective: Telemarket to 200,171 contact records to promote User Conference.• Pre-campaign data analysis identified 1,996 invalid records (1% of total database)• Based on cost per dial, >$3,700 of campaign budget would have been spent calling invalid records.• Total dials placed:! ! ! 249,138• Cost per dial:! ! ! ! $1.86• Post campaign analysis identified 56,171 invalid records representing 28% of total database.• Cost of calling invalid records: >$104,478 Strategies•Develop on-going nurturing process, integrating lead cultivation with a regiment for data re-validation.•Leverage lower cost maintenance procedures leveraging a funnel concept for data validation.•Map internal data to third party for re-validation and or raising of confidence in existing prospects.•When using third party data - pilot multiple sources (measure invalidity cost vs. data costs) (3.5% - 30%).•Scrub data for duplication, competitors, customers, contact validity, bad fits (out of industry scope).•Overall, minimize wasted dollars spent on activity due to irrelevant and inaccurate data.
  8. 8. Pre-Campaign Case 2 Leading Virtualization Firm Aligning Expectation With Available Universe for > 20 City Road Show Strategies•Develop realistic goals based upon expected % conversion and available universe.•Compare existing data coverage on target universe with actual universe.•Too small universe: Re-align expectations where universe is limited.•Too large universe: Look for ways to stratify database based upon key attributes and BI to optimize.
  9. 9. Pre-Campaign Case 3 Fortune 500 Collaboration Firm Refining Target Universe for Demand Generation and Lead Nurturing Initiative• Program Objective: Develop demand generation/lead nurturing initiative to 525 target accounts.• Developed scoring model to measure suspect value and lead status.• Deployed several strategies to enhance data prior to campaign launch: • Mapped target firms to Winn Enterprise Database for contact, demographic, and technology data. • Conducted Internet Research to further enhance account knowledge, • Identified > 3500 related entities linked to target accounts.• > $800,000 booked opportunities converted to sales - additional 100 nurture leads in funnel. What we learned •Developing intelligence prior to program launch can enhance messaging, targeting and prospect stratification. •Intelligence gained prior to program launch also provided better territory alignment. •Developing corporate relationship profiles assisted with cross and up sell opportunities.
  10. 10. Real-Time Analytics• Drive campaign success via the measure of key metrics • Develop front-loaded metrics that can be measured during campaign. • Develop key questions in call guide to help drive BI and key metrics. • Leverage those metrics for innovative practices: • Messaging - message testing • Targeting • Tracking • Lead / Suspect scoring (Suspect Value / Sales Status). • Overall and comparative demand center productivity by rep.
  11. 11. Real-Time Case 1 VAR 500 Firm Appointment Setting / Lead Generation / Lead Nurturing Program• Program Objective: Develop sales-ready opportunities and nurturing leads for on-going initiative.• Challenge: too much data (number of prospect contacts to company ratio).• Winn analyzed the type of functions and titles that were driving higher lead rates.• Winn pre-coded and isolated higher level target contacts and stratified the database.• All other contacts were made viewable in account rep portal for use when referred. What we learned •Reps were better focused on key accounts vs. canvassing too large of an audience. •Campaign lead rates improved by 4X after re-aligning the data.
  12. 12. Real-Time Case 2 Emerging Technology Software Firm Cross-Industry Lead Generation Campaign• Program Objective: Develop sales ready opportunities and nurturing leads for on-going program.• Client unaware of best market to focus on at program launch.• Winn pre-loaded key data metrics including industry classification for all prospects selected for the campaign. What we learned •Relative to resolved records, three industries represented 5X greater lead rate. •Campaign adjusted to focus on remaining prospects in Manufacturing, Services, and Construction. •The more coded data you can pre-load in a campaign, the more opportunities you have to analyze and shift program to drive results.
  13. 13. Post-Campaign Analytics• Leveraging knowledge gained from programs• Key metrics established up-front• Value of data collection and codification of tactical data• Business / competitive intelligence• Re-alignment of messaging and targeting
  14. 14. Post-Campaign Case 1 Fortune 500 Technology Distributor Post-Campaign Analytics to Multiple Events• Objective: Analyze results of 21(on-site) events covering similar technologies over one year.• Analysis of event success by: • Event timing (day/time/season) • Venue type • Lead time for event promotion • Technology Theme• Based upon analysis, leverage knowledge gained to drive future initiatives. What we learned•Special events had the lowest cost per registration (4:1).•Events promoted with a 15+ day lead time out performed those with < days over 50%.•Technology theme dramatically effected cost per registration (as high as 10 X).
  15. 15. Post-Campaign Case 2 Global 100 Technology FirmPost-Campaign Analytics for Appointment Setting / Lead Generation Program• Objective: Analyze results of demand generation initiative.• Analysis of program conducted by: • Data source lead rates • Data source validity rates • Opportunities generated vs. areas of interest• Based upon analysis, leveraged knowledge gained to drive future initiatives. What we learned•Although the average cost of an actionable lead = $151, list sources ranged from < $100 to > $1000.•Re-focus of campaign and future targeting of higher yielding list sources.•Messaging altered to focus on areas resonating with target audience.
  16. 16. Modeling• Data Strategies to Drive Quotas • Using Intelligence from Data • More about the “Ideal Prospect” Algorithm • Strategies for Sourcing and Managing Data
  17. 17. Cost-effective & Successful Customer Acquisition Using Intelligence from Data - Why It’s ImportantTargeting the RIGHT prospects within the organization Making sure the Knowing and Understandingmessage is going to understanding motivators andresonate with the trends and buying concerns of your target persona transitions target buyer
  18. 18. More about the “Ideal Prospect” Algorithm• The Ideal Prospect algorithm is based on the relationship between revenue growth and employee growth.• Developed based on primary research that segmented the enterprise market using some key attitudes and behaviors about enterprises technology needs, how they use it, and what they expect it to do for them.• Leading segment in new technology & adoption, termed “Strategic IT Spenders”: • On average, higher revenue growth & profitability, composing approximately 24% of the global enterprise market & existing in all countries and industries. • Buy IT for productivity gains more than cost savings. • Perceive their organizations to be highly dependent on real-time transactions in daily operations, and involve many different channels for both IT information and sales. • Always on the lookout for new technologies that make them more efficient.""
  19. 19. Strategies for Sourcing and Managing DataProject Goals, Budgets, & Timelines •Variety of possible places • POS data from the channel • CRM data • Financial dataInternal Data External 3rd Primary Manually Modeled • Harte-Hanks Party Data Research Collected Data • Jigsaw • D & B, Hoovers • US census • Postal zip maps
  20. 20. Case Study 1 Data Center Switch Vendor• Problem: Build sales funnel• Solution: Create highly targeted list of enterprises with 800 company names based on Ideal Prospect Algorithm, developed using primary research segmentation model. • Focus 4-quarter demand generation program using demand center tactics to nurture list of OWNED names.
  21. 21. Case Study 1 Program Specifics• Filling the sales funnel tightly couples data and outbound marketing programs. • The process begins with defining and building a highly targeted Enterprise target list to nurture. • In partnership with the Demand Center, populate the contact list, and deliver a set amount of A, B and C leads.
  22. 22. Case Study 1 Results WaveLength Standard Approach eDM ApproachList Size Requirements based on a 1800 names 50,000 names2% response rateCost Per Name for Rental 0.90Total List Rental Fees $45,0001 Inside sales rep to qualify leads 12.5 days @ $320/day or $4.57/based on 80 calls per day call = a total of 4000.00WL Program $32,000Sales-accepted leads 99 22.7Cost per qualified sales lead $323.23 based on 80% of the $2,158.75 project being completed- cost will go down as reach our final lead goalsTOTAL COSTS $32,000 $49,000
  23. 23. Collaborative Case Study Winn-WaveLength Cloud Computing Primary Research• Goal: Gain an in-depth understanding of the fast evolving cloud marketplace. We looked at buying trends, buyers and influencers personas, and the role of the channel.• Some results: Nearly 42% of sample of medium to large enterprises (n=151) are deploying or testing some type of cloud model.
  24. 24. Collaborative Case Study Cloud Computing Primary Research• Cloud buying habits of the early market: Target the right persona • Internal IT plays largest role – from creating the strategy to developing, migrating and managing application. • Top business management is highly involved for current cloud users. • Role of trusted, third party partners in enterprise buying currently very limited among early cloud adopting enterprises. • Systems Integrator and Consulting Partners. • Software vendor partners. • And NOT telco service providers or hardware vendors.
  25. 25. Collaborative Case Study Cloud Computing Primary Research• Created demand generation message based on motivators in why the cloud transition is happening: • Operating cost reduction • Rapid application deployment/more nimble IT• Addressed concerns about deploying cloud services in demand generation campaign: • Security breaches for applications and for stored data (a.k.a at rest) • Perceptions of high start-up costs • Lack of “trusted” 3rd party relationship
  26. 26. Summing it up...• It takes a whole lot of leads to get to a few sales, so make sure you’re targeting your RIGHT market, in the RIGHT way and getting the most out of your efforts.• Data is key to your success in your campaign - for targeting, messaging, and addressing concerns in an evolving market.• Enterprise Migration to the Cloud study is a perfect example of how research helps: • Target top IT executives • Mitigate adoption concerns, e.g., security issues, and high start-up costs • Demonstrate how cloud solutions results in cost savings and faster application deployment
  27. 27. Thank you very much for joining us today! Q/Awinntech.net wlanalytics.com

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